
Proceedings Paper
Multi-polarimetric textural distinctiveness for outdoor robotic saliency detectionFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
Mobile robots that rely on vision, for navigation and object detection, use saliency approaches to identify a set
of potential candidates to recognize. The state of the art in saliency detection for mobile robotics often rely upon
visible light imaging, using conventional camera setups, to distinguish an object against its surroundings based
on factors such as feature compactness, heterogeneity and/or homogeneity. We are demonstrating a novel multi-
polarimetric saliency detection approach which uses multiple measured polarization states of a scene. We leverage
the light-material interaction known as Fresnel reflections to extract rotationally invariant multi-polarimetric
textural representations to then train a high dimensional sparse texture model. The multi-polarimetric textural
distinctiveness is characterized using a conditional probability framework based on the sparse texture model
which is then used to determine the saliency at each pixel of the scene. It was observed that through the
inclusion of additional polarized states into the saliency analysis, we were able to compute noticeably improved
saliency maps in scenes where objects are difficult to distinguish from their background due to color intensity
similarities between the object and its surroundings.
Paper Details
Date Published: 8 February 2015
PDF: 7 pages
Proc. SPIE 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 94060A (8 February 2015); doi: 10.1117/12.2082860
Published in SPIE Proceedings Vol. 9406:
Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)
PDF: 7 pages
Proc. SPIE 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 94060A (8 February 2015); doi: 10.1117/12.2082860
Show Author Affiliations
S. A. Haider, Univ. of Waterloo (Canada)
C. Scharfenberger, Univ. of Waterloo (Canada)
F. Kazemzadeh, Univ. of Waterloo (Canada)
C. Scharfenberger, Univ. of Waterloo (Canada)
F. Kazemzadeh, Univ. of Waterloo (Canada)
Published in SPIE Proceedings Vol. 9406:
Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)
© SPIE. Terms of Use
